Utilizing Stakeholder Consultations to Identify Context-Specific Professional Skills for Veterinary Graduates in Bangladesh
Bibliographic record
Abstract
Despite professional skills being part of the Day One Competences published by national as well as international accreditation bodies, veterinary schools in Bangladesh have limited associated teaching within their curricula. Therefore, our study aimed to identify the most important professional skills for veterinarians in Bangladesh through local consultation to inform future initiatives to change the curriculum. Eleven focus groups were conducted with 45 stakeholders who included veterinarians who supervise students on workplacements, faculty, recent graduates, final year students, and clients. The audio recordings were transcribed, translated into English from Bengali and analyzed using an inductive thematic approach. Professional skills were considered essential by all stakeholder groups. The most important professional skills were identified as communication, ethical conduct, teamwork, career options, financial management skills, lifelong learning, time management and self-appraisal. One of the best opportunities to practice many of the skills was identified as being during final year workplacements, while participating in extracurricular activities, learning by observing others and self-motivation were also considered valuable. Participants identified a need for more formal professional skills teaching within the curriculum. Challenges included finding space in the curriculum, raising awareness amongst university academics and engaging students and faculty in the new initiatives. This study has identified the most important professional skills in our context. Consultation with relevant regional stakeholders was crucial and will inform curricular change. The results are being used in the development of professional skills courses with the long-term aim of better preparing our graduates for their future careers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".